U.S. patent application number 16/742045 was filed with the patent office on 2020-05-21 for detecting an event from signal data.
The applicant listed for this patent is Banjo, Inc.. Invention is credited to Rish Mehta, Damien Patton.
Application Number | 20200162534 16/742045 |
Document ID | / |
Family ID | 68982194 |
Filed Date | 2020-05-21 |
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United States Patent
Application |
20200162534 |
Kind Code |
A1 |
Mehta; Rish ; et
al. |
May 21, 2020 |
DETECTING AN EVENT FROM SIGNAL DATA
Abstract
The present invention extends to methods, systems, and computer
program products for detecting events from signal data. A signal is
ingested. A first score is computed from a selected portion of the
signal. The first score indicates a likelihood of the signal
including information related to an event type. It is determined
that processing of another signal is warranted based on the
indicated likelihood. Resources are allocated to process the other
signal. The other signal is ingested. Parameters associated with
the other signal are accessed. A second score is computed from the
parameters utilizing the allocated resources. A previously
unidentified event of the event type is identified based on the
second score and utilizing the allocated resources. An entity is
electronically notified about the previously unidentified
event.
Inventors: |
Mehta; Rish; (Redwood City,
CA) ; Patton; Damien; (Park City, UT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Banjo, Inc. |
South Jordan |
UT |
US |
|
|
Family ID: |
68982194 |
Appl. No.: |
16/742045 |
Filed: |
January 14, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16560238 |
Sep 4, 2019 |
10581945 |
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16742045 |
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16390297 |
Apr 22, 2019 |
10447750 |
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16560238 |
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16101208 |
Aug 10, 2018 |
10313413 |
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16390297 |
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62550797 |
Aug 28, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 65/601 20130101;
H04L 65/602 20130101; H04L 67/10 20130101; H04L 67/02 20130101;
G06F 9/5027 20130101; G06F 9/542 20130101; H04L 65/604 20130101;
H04L 65/4084 20130101 |
International
Class: |
H04L 29/06 20060101
H04L029/06; G06F 9/50 20060101 G06F009/50 |
Claims
1. A method comprising: ingesting a signal; computing a first score
from a portion of the signal and indicating a likelihood of the
signal including event information related to an event type;
determining processing of another signal is warranted based on the
indicated likelihood; allocating computing resources to process the
other signal; computing a second score from parameters associated
with the other signal utilizing the allocated computing resources;
detecting a previously unidentified event of the event type based
on the second score; and electronically notifying a computing
device about the previously unidentified event.
2. The method of claim 1, wherein ingesting a signal comprises
ingesting a streaming signal; and wherein computing a first score
from a portion of the signal comprises computing a first score from
a portion of the streaming signal.
3. The method of claim 1, wherein ingesting a signal comprises
ingesting a non-streaming signal; and wherein computing a first
score from a portion of the signal comprises computing a first
score from a portion of the non-streaming signal.
4. The method of claim 1, wherein ingesting a signal comprises
ingesting a database signal; wherein computing a first score from a
portion of the signal comprises computing a first score from a
portion of the database signal; and wherein detecting a previously
unidentified event of the event type comprises detecting an
available bed at a treatment facility.
5. The method of claim 1, wherein computing a second score from
parameters associated with the other signal comprises computing a
second score from parameters associated with a database signal; and
wherein detecting a previously unidentified event of the event type
comprises detecting an available bed at a treatment facility.
6. The method of claim 1, wherein computing a first score from a
portion of the data stream comprises computing a first score from a
portion of an audio stream; and wherein computing a second score
from parameters associated with a portion of the other data stream
comprises computing the second score from parameters associated
with one of: a text stream, a video stream, or a sensor data
stream.
7. The method of claim 1, wherein computing a first score from a
portion of the data stream comprises computing a first score from a
portion of a text stream; and wherein computing a second score from
parameters associated with a portion of the other data stream
comprises computing the second score from parameters associated
with one of: an audio stream, a video stream, or a sensor data
stream.
8. The method of claim 1, wherein computing a first score from a
portion of the data stream comprises computing a first score from a
portion of a video stream; and wherein computing a second score
from parameters associated with a portion of the other data stream
comprises computing the second score from parameters associated
with one of: an audio stream, a text stream, or a sensor data
stream.
9. The method of claim 1, wherein computing a first score from a
portion of the data stream comprises computing a first score from a
portion of a sensor data stream; and wherein computing a second
score from parameters associated with a portion of the other data
stream comprises computing the second score from parameters
associated with one of: an audio stream, a video stream, or a text
stream.
10. The method of claim 1, wherein computing a first score
comprises computing a first score indicating a likelihood of the
signal including event information related to an event type
occurring within a geographic region; and wherein detecting a
previously unidentified event comprises detecting a previously
unidentified event within the geographic region.
11. The method of claim 1, wherein determining processing of
another signal is warranted based on the indicated likelihood
comprises determining that the other signal is related to the
signal along one or more of a time dimension, a location dimension,
or a context dimension.
12. A system comprising: a processor; system memory coupled to the
processor and storing instructions configured to cause the
processor to: ingest a signal; compute a first score from a portion
of the signal and indicating a likelihood of the signal including
event information related to an event type; determine processing of
another signal is warranted based on the indicated likelihood;
allocate computing resources to process the other signal; compute a
second score from parameters associated with the other signal
utilizing the allocated computing resources; detect a previously
unidentified event of the event type based on the second score; and
electronically notify a computing device about the previously
unidentified event.
13. The system of claim 12, wherein instructions configured to
ingest a signal comprise instructions configured to ingest a
streaming signal; and wherein instructions configured to compute a
first score from a portion of the signal comprise instructions
configured to compute a first score from a portion of the streaming
signal.
14. The system of claim 12, wherein instructions configured to
ingest a signal comprise instructions configured to ingest a
non-streaming signal; and wherein instructions configured to
compute a first score from a portion of the signal comprise
instructions configured to compute a first score from a portion of
the non-streaming signal.
15. The system of claim 12, wherein instructions configured to
ingest a signal comprise instructions configured to ingest a
database signal; wherein instructions configured to compute a first
score from a portion of the signal comprise instructions configured
to compute a first score from a portion of the database signal; and
wherein instructions configured to detect a previously unidentified
event of the event type comprise instructions configured to detect
an available bed at a treatment facility.
16. The system of claim 12, wherein instructions configured to
compute a second score from parameters associated with the other
signal comprise instructions configured to compute a second score
from parameters associated with a database signal; and wherein
instructions configured to detect a previously unidentified event
of the event type comprise instructions configured to detect an
available bed at a treatment facility.
17. The system of claim 12, wherein instructions configured to
compute a first score from a portion of the data stream comprise
instructions configured to compute a first score from a portion of
an audio stream; and wherein instructions configured to compute a
second score from parameters associated with a portion of the other
data stream comprise instructions configured to compute the second
score from parameters associated with one of: a text stream, a
video stream, or a sensor data stream.
18. The system of claim 12, wherein instructions configured to
compute a first score from a portion of the data stream comprise
instructions configured to compute a first score from a portion of
a text stream; and wherein instructions configured to compute a
second score from parameters associated with a portion of the other
data stream comprises instructions configured to compute the second
score from parameters associated with one of: an audio stream, a
video stream, or a sensor data stream.
19. The system of claim 12, wherein instructions configured to
compute a first score from a portion of the data stream comprise
instructions configured to compute a first score from a portion of
a video stream; and wherein instructions configured to compute a
second score from parameters associated with a portion of the other
data stream comprise instructions configured to compute the second
score from parameters associated with one of: an audio stream, a
text stream, or a sensor data stream.
20. The system of claim 12, wherein instructions configured to
compute a first score from a portion of the data stream comprise
instructions configured to compute a first score from a portion of
a sensor data stream; and wherein instructions configured to
compute a second score from parameters associated with a portion of
the other data stream comprise instructions configured to compute
the second score from parameters associated with one of: an audio
stream, a video stream, or a text stream.
21. The system of claim 12, wherein instructions configured to
compute a first score comprise instructions configured to compute a
first score indicating a likelihood of the signal including event
information related to an event type occurring within a geographic
region; and wherein instructions configured to detect a previously
unidentified event comprise instructions configured to detect a
previously unidentified event within the geographic region.
22. The system of claim 12, wherein instructions configured to
determine processing of another signal is warranted based on the
indicated likelihood comprises instructions configured to determine
that the other signal is related to the signal along one or more of
a time dimension, a location dimension, or a context dimension.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Continuation of U.S. patent
application Ser. No. 16/560,238, entitled "Detecting An Event From
Signal Data", filed Sep. 4, 2019, which is incorporated herein in
its entirety. That application is a Continuation-In-Part of U.S.
patent application Ser. No. 16/390,297, now U.S. Pat. No.
10,447,750, entitled "Detecting Events From Ingested Communication
Signals", filed Apr. 22, 2019 which is incorporated herein in its
entirety. That application is a Continuation of U.S. patent
application Ser. No. 16/101,208, now U.S. Pat. No. 10,313,413,
entitled "Detecting Events From Ingested Communication Streams",
filed Aug. 10, 2018 which is incorporated herein in its entirety.
That application claims the benefit of U.S. Provisional Patent
Application Ser. No. 62/550,797, entitled "Event Detection System
and Method", filed Aug. 28, 2017 which is incorporated herein in
its entirety.
[0002] This application is related to U.S. Provisional Patent
Application Ser. No. 62/664,001, entitled "Normalizing Different
Types Of Ingested Signals Into A Common Format", filed Apr. 27,
2018 which is incorporated herein in its entirety. This application
is related to U.S. Provisional Patent Application Ser. No.
62/667,616, entitled "Normalizing Different Types Of Ingested
Signals Into A Common Format", filed May 7, 2018 which is
incorporated herein in its entirety. This application is related to
U.S. Provisional Patent Application Ser. No. 62/686,791 entitled,
"Normalizing Signals", filed Jun. 19, 2018 which is incorporated
herein in its entirety.
BACKGROUND
1. Background and Relevant Art
[0003] Data provided to computer systems can come from any number
of different sources, such as, for example, user input, files,
databases, applications, sensors, social media systems, cameras,
emergency communications, etc. In some environments, computer
systems receive (potentially large volumes of) data from a variety
of different domains and/or verticals in a variety of different
formats. When data is received from different sources and/or in
different formats, it can be difficult to efficiently and
effectively derive intelligence from the data.
[0004] Extract, transform, and load (ETL) refers to a technique
that extracts data from data sources, transforms the data to fit
operational needs, and loads the data into an end target. ETL
systems can be used to integrate data from multiple varied sources,
such as, for example, from different vendors, hosted on different
computer systems, etc.
[0005] ETL is essentially an extract and then store process. Prior
to implementing an ETL solution, a user defines what (e.g., subset
of) data is to be extracted from a data source and a schema of how
the extracted data is to be stored. During the ETL process, the
defined (e.g., subset of) data is extracted, transformed to the
form of the schema (i.e., schema is used on write), and loaded into
a data store. To access different data from the data source, the
user has to redefine what data is to be extracted. To change how
data is stored, the user has to define a new schema.
[0006] ETL is beneficially because it allows a user to access a
desired portion of data in a desired format. However, ETL can be
cumbersome as data needs evolve. Each change to the extracted data
and/or the data storage results in the ETL process having to be
restarted. Further, ETL can be difficult to implement with
streaming data types.
BRIEF SUMMARY
[0007] Examples extend to methods, systems, and computer program
products for detecting events from signal data. A signal is
ingested. A portion of the signal is selected from within the
signal. A first score is computed from the selected portion. The
first score indicates a likelihood of the signal including
information related to an event type.
[0008] It is determined that processing of another signal is
warranted based on the indicated likelihood. Resources are
allocated to process the other signal. The other signal is
ingested. Parameters associated with the other signal are accessed.
A second score is computed from the parameters utilizing the
allocated resources. A previously unidentified event of the event
type is identified based on the second score and utilizing the
allocated resources. An entity is electronically notified about the
previously unidentified event.
[0009] This summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used as an aid in determining the scope of
the claimed subject matter.
[0010] Additional features and advantages will be set forth in the
description which follows, and in part will be obvious from the
description, or may be learned by practice. The features and
advantages may be realized and obtained by means of the instruments
and combinations particularly pointed out in the appended claims.
These and other features and advantages will become more fully
apparent from the following description and appended claims, or may
be learned by practice as set forth hereinafter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] In order to describe the manner in which the above-recited
and other advantages and features can be obtained, a more
particular description will be rendered by reference to specific
implementations thereof which are illustrated in the appended
drawings. Understanding that these drawings depict only some
implementations and are not therefore to be considered to be
limiting of its scope, implementations will be described and
explained with additional specificity and detail through the use of
the accompanying drawings in which:
[0012] FIG. 1A illustrates an example computer architecture that
facilitates normalizing ingesting signals.
[0013] FIG. 1B illustrates an example computer architecture that
facilitates detecting events from normalized signals.
[0014] FIG. 2 illustrates a flow chart of an example method for
normalizing ingested signals.
[0015] FIG. 3 illustrates a flow chart of an example method for
ingesting a communication stream and detecting an event.
[0016] FIG. 4 illustrates an example computer architecture that
facilitates generating event scores from ingested communication
streams.
[0017] FIG. 5 illustrates a flow chart of an example method for
generating an event score from an ingested communication
stream.
[0018] FIGS. 6A and 6B are example syntaxes for a first and a
second communication channel, respectively.
[0019] FIG. 7 is an example of event scoring based on historic
parameter values for a geographic region.
[0020] FIG. 8 a computer architecture that facilitates concurrently
handling communication signals from a plurality of channels.
[0021] FIG. 9 illustrates an example computer architecture that
facilitates detecting an event from scores generated from ingested
signals.
[0022] FIG. 10 illustrates a flow chart of an example method for
detecting an event from scores generated from ingested signals.
DETAILED DESCRIPTION
[0023] Examples extend to methods, systems, and computer program
products for detecting events from ingested communication
streams.
[0024] Entities (e.g., parents, other family members, guardians,
friends, teachers, social workers, first responders, hospitals,
delivery services, media outlets, government entities, etc.) may
desire to be made aware of relevant events as close as possible to
the events' occurrence (i.e., as close as possible to "moment
zero"). Different types of ingested signals (e.g., social media
signals, web signals, and streaming signals) can be used to detect
events. Aspects of the invention ingest and process a plurality of
audio streams, such as audio streams from communication channels
used by local municipalities, counties, states, countries, or other
regional subdivisions. Signal ingestion and processing can be
performed in real- or near-real time (e.g., with no or near-zero
latency), but can alternatively or additionally be performed
asynchronously from audio stream generation or receipt.
[0025] More particularly, in one aspect, an event is detected based
on characteristics of a communication (e.g., audio) stream. For
example, an emergency event of interest, such as a shooting,
bombing, riot, or other emergency event, that has (or will) occur
is detected based on analysis of emergency communications. In
another example, logistical parameters (e.g., location, time,
operating context, etc.) are determined for each operator within an
operator fleet. In a further example, logistical parameters, such
as, bed availability, are determined for one or more public and/or
private care/treatment facilities. Event parameters, such as the
event location (e.g., specific geolocation, geographic region,
etc.), the event time (e.g., start and/or stop), the event
severity, event participants, or any other suitable parameter can
also be determined. These event parameters can be fed into an event
detection system that can be used to: gather additional information
about the event, to notify entities, such as emergency personnel,
security personnel, financial institutions, or other entities
interested in the event, to reroute fleet vehicles, etc.
[0026] A variety of challenges arise when processing streaming
communication channels and other signals. A relatively large amount
of computing resources (e.g., uptime and power) may be consumed to
continually monitor and analyze communication on a communication
channel. Resource consumption scales directly with the number of
communication channels monitored and becomes commercially and
technically unfeasible after a threshold number of communication
channels are ingested. Further, different jurisdictions,
corporations, etc. can use different syntax, vocabulary, and/or
language from other jurisdictions, corporations, etc., which
reduces or eliminates the ability to process multiple communication
channels using the same processes (and further increasing the
amount of required computing resources).
[0027] Communications on said channels also tend to be noisy, which
decrease the quality of the event signal extracted from said
communications. Additionally, a single communication channel can
concurrently have multiple ongoing conversations (e.g., between the
dispatcher and multiple field operators), which may need to be
separated out for event analysis. Furthermore, not all of the
conversations are relevant (e.g., about an event). For example,
some conversations may be idle banter, while others may be about
low-level incidents that do not rise to the level of an event.
[0028] Aspects of the invention mitigate, reduce, and potentially
fully resolve one or more of the described challenges. A smaller
portion of a signal, for example, a clip or sub-clip, is examined.
Based on the likelihood of the smaller portion containing event
related information, a decision is made whether to examine the
signal more thoroughly or to examine another (possibly related)
signal. That is, a less resource intensive examination of a signal
is performed to estimate the benefit of performing a subsequent
more resource intensive examination of the signal or of another
signal.
[0029] For example, aspects can decrease computing resource
consumption by identifying and analyzing smaller portions of a
signal (as opposed to larger portions or the entire signal). This
minimizes compute power by reducing the volume of data to be
analyzed (e.g., a subset of the signal instead of the entire
signal). Additionally, or alternatively, computing resource
consumption is decreased by pre-evaluating parts of a signal for an
event content probability. The event content probability can then
be used to determine the potential benefit of more thoroughly
examining the signal or another signal.
[0030] Examining signal portions also conserves compute power and
time resources by limiting application of resource-intensive
processes (e.g., signal interpretation, voice recognition, NLP,
etc.) to a subset of portions with a higher probability of having
event-associated content. Additionally, or alternatively, computing
resource consumption is reduced by leveraging standardized syntax
for each jurisdiction, corporation, communication channel, or
dispatcher. Processing of signals can focus on signal portions with
the highest probability of having an event identifier (e.g., event
code which gives an indication of whether the clip is about an
event, the event type, and/or event severity).
[0031] In one aspect, different analysis modules can be used for
each different communication channel. Using different analysis
modules increases the processing speed of each communication
stream, since smaller, lighter-weight modules can be used for each
clip analysis instead of using a larger, slower module that
accommodates nuances of multiple communication channels. In one
example, each communication channel can be associated with its own
module(s) that identifies the event identifier clip or sub-clip,
extracts event parameters from the clip or sub-clip, or performs
any other analysis. These modules are preferably trained on
historic communication clips from the respective communication
channel but can be trained using other communication clips or
otherwise trained.
[0032] Noise-reduction processes can be applied on the
communication streams before the clips are identified, which
functions to clean up the subsequently processed signal. For
example, the system and method can remove white noise, static,
scratches, pops, chirps, or other features prior to clip analysis.
Conversation analysis (e.g., NLP analysis), voice recognition, or
other techniques to can be applied to stitch together different
conversations on the same communication channel.
[0033] In general, signal ingestion modules ingest different types
of raw structured and/or raw unstructured signals on an ongoing
basis. Different types of signals can include different data media
types and different data formats. Data media types can include
audio, video, image, and text. Different formats can include text
in XML, text in JavaScript Object Notation (JSON), database
formats, text in RSS feed, plain text, video stream in Dynamic
Adaptive Streaming over HTTP (DASH), video stream in HTTP Live
Streaming (HLS), video stream in Real-Time Messaging Protocol
(RTMP), other Multipurpose Internet Mail Extensions (MIME) types,
audio stream in Free Lossless Audio Codec ("FLAC"), audio stream in
Waveform Audio File Format (WAV), audio stream in Apple Lossless
Audio Codec ("ALAC") etc. Handling different types and formats of
data introduces inefficiencies into subsequent event detection
processes, including when determining if different signals relate
to the same event.
[0034] Accordingly, the signal ingestion modules can normalize raw
signals across multiple data dimensions to form normalized signals.
Each dimension can be a scalar value or a vector of values. In one
aspect, raw signals are normalized into normalized signals having a
Time, Location, Context (or "TLC") dimensions.
[0035] A Time (T) dimension can include a time of origin or
alternatively a "event time" of a signal. A Location (L) dimension
can include a location anywhere across a geographic area, such as,
a country (e.g., the United States), a State, a defined area, an
impacted area, an area defined by a geo cell, an address, etc.
[0036] A Context (C) dimension indicates circumstances surrounding
formation/origination of a raw signal in terms that facilitate
understanding and assessment of the raw signal. The Context (C)
dimension of a raw signal can be derived from express as well as
inferred signal features of the raw signal.
[0037] Signal ingestion modules can include one or more single
source classifiers. A single source classifier can compute a single
source probability for a raw signal from features of the raw
signal. A single source probability can reflect a mathematical
probability or approximation of a mathematical probability (e.g., a
percentage between 0%-100%) of an event actually occurring. A
single source classifier can be configured to compute a single
source probability for a single event type or to compute a single
source probability for each of a plurality of different event
types. A single source classifier can compute a single source
probability using artificial intelligence, machine learning, neural
networks, logic, heuristics, etc.
[0038] As such, single source probabilities and corresponding
probability details can represent a Context (C) dimension.
Probability details can indicate (e.g., can include a hash field
indicating) a probabilistic model and (express and/or inferred)
signal features considered in a signal source probability
calculation.
[0039] Thus, per signal type, signal ingestion modules determine
Time (T), a Location (L), and a Context (C) dimensions associated
with a signal. Different ingestion modules can be utilized/tailored
to determine T, L, and C dimensions associated with different
signal types. Normalized (or "TLC") signals can be forwarded to an
event detection infrastructure. When signals are normalized across
common dimensions subsequent event detection is more efficient and
more effective.
[0040] Normalization of ingestion signals can include
dimensionality reduction. Generally, "transdimensionality"
transformations can be structured and defined in a "TLC"
dimensional model. Signal ingestion modules can apply the
"transdimensionality" transformations to generic source data in raw
signals to re-encode the source data into normalized data having
lower dimensionality. Thus, each normalized signal can include a T
vector, an L vector, and a C vector. At lower dimensionality, the
complexity of measuring "distances" between dimensional vectors
across different normalized signals is reduced.
[0041] Concurrently with signal ingestion, an event detection
infrastructure considers features of different combinations of
normalized signals to attempt to identify events of interest to
various parties. For example, the event detection infrastructure
can determine that features of multiple different normalized
signals collectively indicate an event of interest to one or more
parties. Alternately, the event detection infrastructure can
determine that features of one or more normalized signals indicate
a possible event of interest to one or more parties. The event
detection infrastructure then determines that features of one or
more other normalized signals validate the possible event as an
actual event of interest to the one or more parties. Signal
features can include: signal type, signal source, signal content,
Time (T) dimension, Location (L) dimension, Context (C) dimension,
other circumstances of signal creation, etc.
[0042] In one aspect, streaming communication signals are received
on one or more communication channels. Characteristics of the
streaming communication signals can be used (potentially in
combination with other signals) to detect events.
[0043] Implementations can comprise or utilize a special purpose or
general-purpose computer including computer hardware, such as, for
example, one or more computer and/or hardware processors (including
any of Central Processing Units (CPUs), and/or Graphical Processing
Units (GPUs), general-purpose GPUs (GPGPUs), Field Programmable
Gate Arrays (FPGAs), application specific integrated circuits
(ASICs), Tensor Processing Units (TPUs)) and system memory, as
discussed in greater detail below. Implementations also include
physical and other computer-readable media for carrying or storing
computer-executable instructions and/or data structures. Such
computer-readable media can be any available media that can be
accessed by a general purpose or special purpose computer system.
Computer-readable media that store computer-executable instructions
are computer storage media (devices). Computer-readable media that
carry computer-executable instructions are transmission media.
Thus, by way of example, and not limitation, implementations can
comprise at least two distinctly different kinds of
computer-readable media: computer storage media (devices) and
transmission media.
[0044] Computer storage media (devices) includes RAM, ROM, EEPROM,
CD-ROM, Solid State Drives ("SSDs") (e.g., RAM-based or
Flash-based), Shingled Magnetic Recording ("SMR") devices, Flash
memory, phase-change memory ("PCM"), other types of memory, other
optical disk storage, magnetic disk storage or other magnetic
storage devices, or any other medium which can be used to store
desired program code means in the form of computer-executable
instructions or data structures and which can be accessed by a
general purpose or special purpose computer.
[0045] In one aspect, one or more processors are configured to
execute instructions (e.g., computer-readable instructions,
computer-executable instructions, etc.) to perform any of a
plurality of described operations. The one or more processors can
access information from system memory and/or store information in
system memory. The one or more processors can (e.g., automatically)
transform information between different formats, such as, for
example, between any of: raw signals, streaming signals,
communication signals, audio signals, normalized signals, search
terms, geo cell subsets, events, clips, sub-clips, clip scores,
event identifiers, parameters, event scores, probabilities,
notifications, etc.
[0046] System memory can be coupled to the one or more processors
and can store instructions (e.g., computer-readable instructions,
computer-executable instructions, etc.) executed by the one or more
processors. The system memory can also be configured to store any
of a plurality of other types of data generated and/or transformed
by the described components, such as, for example, raw signals,
streaming signals, communication signals, audio signals, normalized
signals, search terms, geo cell subsets, events, clips, sub-clips,
clip scores, event identifiers, parameters, event scores,
probabilities, notifications, etc.
[0047] A "network" is defined as one or more data links that enable
the transport of electronic data between computer systems and/or
modules and/or other electronic devices. When information is
transferred or provided over a network or another communications
connection (either hardwired, wireless, or a combination of
hardwired or wireless) to a computer, the computer properly views
the connection as a transmission medium. Transmissions media can
include a network and/or data links which can be used to carry
desired program code means in the form of computer-executable
instructions or data structures and which can be accessed by a
general purpose or special purpose computer. Combinations of the
above should also be included within the scope of computer-readable
media.
[0048] Further, upon reaching various computer system components,
program code means in the form of computer-executable instructions
or data structures can be transferred automatically from
transmission media to computer storage media (devices) (or vice
versa). For example, computer-executable instructions or data
structures received over a network or data link can be buffered in
RAM within a network interface module (e.g., a "NIC"), and then
eventually transferred to computer system RAM and/or to less
volatile computer storage media (devices) at a computer system.
Thus, it should be understood that computer storage media (devices)
can be included in computer system components that also (or even
primarily) utilize transmission media.
[0049] Computer-executable instructions comprise, for example,
instructions and data which, in response to execution at a
processor, cause a general purpose computer, special purpose
computer, or special purpose processing device to perform a certain
function or group of functions. The computer executable
instructions may be, for example, binaries, intermediate format
instructions such as assembly language, or even source code.
Although the subject matter has been described in language specific
to structural features and/or methodological acts, it is to be
understood that the subject matter defined in the appended claims
is not necessarily limited to the described features or acts
described above. Rather, the described features and acts are
disclosed as example forms of implementing the claims.
[0050] Those skilled in the art will appreciate that the described
aspects may be practiced in network computing environments with
many types of computer system configurations, including, personal
computers, desktop computers, laptop computers, message processors,
hand-held devices, wearable devices, multicore processor systems,
multi-processor systems, microprocessor-based or programmable
consumer electronics, network PCs, minicomputers, mainframe
computers, mobile telephones, PDAs, tablets, routers, switches, and
the like. The described aspects may also be practiced in
distributed system environments where local and remote computer
systems, which are linked (either by hardwired data links, wireless
data links, or by a combination of hardwired and wireless data
links) through a network, both perform tasks. In a distributed
system environment, program modules may be located in both local
and remote memory storage devices.
[0051] Further, where appropriate, functions described herein can
be performed in one or more of: hardware, software, firmware,
digital components, or analog components. For example, one or more
Field Programmable Gate Arrays (FPGAs) and/or one or more
application specific integrated circuits (ASICs) and/or one or more
Tensor Processing Units (TPUs) can be programmed to carry out one
or more of the systems and procedures described herein. Hardware,
software, firmware, digital components, or analog components can be
specifically tailor-designed for a higher speed detection or
artificial intelligence that can enable signal processing. In
another example, computer code is configured for execution in one
or more processors, and may include hardware logic/electrical
circuitry controlled by the computer code. These example devices
are provided herein purposes of illustration, and are not intended
to be limiting. Embodiments of the present disclosure may be
implemented in further types of devices.
[0052] The described aspects can also be implemented in cloud
computing environments. In this description and the following
claims, "cloud computing" is defined as a model for enabling
on-demand network access to a shared pool of configurable computing
resources. For example, cloud computing can be employed in the
marketplace to offer ubiquitous and convenient on-demand access to
the shared pool of configurable computing resources (e.g., compute
resources, networking resources, and storage resources). The shared
pool of configurable computing resources can be provisioned via
virtualization and released with low effort or service provider
interaction, and then scaled accordingly.
[0053] A cloud computing model can be composed of various
characteristics such as, for example, on-demand self-service, broad
network access, resource pooling, rapid elasticity, measured
service, and so forth. A cloud computing model can also expose
various service models, such as, for example, Software as a Service
("SaaS"), Platform as a Service ("PaaS"), and Infrastructure as a
Service ("IaaS"). A cloud computing model can also be deployed
using different deployment models such as private cloud, community
cloud, public cloud, hybrid cloud, and so forth. In this
description and in the following claims, a "cloud computing
environment" is an environment in which cloud computing is
employed.
[0054] In this description and the following claims, a "geo cell"
is defined as a piece of "cell" in a spatial grid in any form. In
one aspect, geo cells are arranged in a hierarchical structure.
Cells of different geometries can be used.
[0055] A "geohash" is an example of a "geo cell".
[0056] In this description and the following claims, "geohash" is
defined as a geocoding system which encodes a geographic location
into a short string of letters and digits. Geohash is a
hierarchical spatial data structure which subdivides space into
buckets of grid shape (e.g., a square). Geohashes offer properties
like arbitrary precision and the possibility of gradually removing
characters from the end of the code to reduce its size (and
gradually lose precision). As a consequence of the gradual
precision degradation, nearby places will often (but not always)
present similar prefixes. The longer a shared prefix is, the closer
the two places are. geo cells can be used as a unique identifier
and to approximate point data (e.g., in databases).
[0057] In one aspect, a "geohash" is used to refer to a string
encoding of an area or point on the Earth. The area or point on the
Earth may be represented (among other possible coordinate systems)
as a latitude/longitude or Easting/Northing--the choice of which is
dependent on the coordinate system chosen to represent an area or
point on the Earth. geo cell can refer to an encoding of this area
or point, where the geo cell may be a binary string comprised of 0s
and is corresponding to the area or point, or a string comprised of
0s, 1s, and a ternary character (such as X)--which is used to refer
to a don't care character (0 or 1). A geo cell can also be
represented as a string encoding of the area or point, for example,
one possible encoding is base-32, where every 5 binary characters
are encoded as an ASCII character.
[0058] Depending on latitude, the size of an area defined at a
specified geo cell precision can vary. When geohash is used for
spatial indexing, the areas defined at various geo cell precisions
are approximately:
TABLE-US-00001 TABLE 1 Example Areas at Various Geo Cell Precisions
geo cell Length/Precision width .times. height 1 5,009.4 km .times.
4,992.6 km 2 1,252.3 km .times. 624.1 km.sup. 3 156.5 km .times.
156 km.sup. 4 39.1 km .times. 19.5 km 5 4.9 km .times. 4.9 km 6 1.2
km .times. 609.4 m 7 152.9 m .times. 152.4 m 8 38.2 m .times. 19
m.sup. 9 4.8 m .times. 4.8 m 10 1.2 m .times. 59.5 cm 11 14.9 cm
.times. 14.9 cm 12 3.7 cm .times. 1.9 cm
[0059] Other geo cell geometries, such as, hexagonal tiling,
triangular tiling, etc. are also possible. For example, the H3
geospatial indexing system is a multi-precision hexagonal tiling of
a sphere (such as the Earth) indexed with hierarchical linear
indexes.
[0060] In another aspect, geo cells are a hierarchical
decomposition of a sphere (such as the Earth) into representations
of regions or points based a Hilbert curve (e.g., the S2 hierarchy
or other hierarchies). Regions/points of the sphere can be
projected into a cube and each face of the cube includes a
quad-tree where the sphere point is projected into. After that,
transformations can be applied and the space discretized. The geo
cells are then enumerated on a Hilbert Curve (a space-filling curve
that converts multiple dimensions into one dimension and preserves
the approximate locality).
[0061] Due to the hierarchical nature of geo cells, any signal,
event, entity, etc., associated with a geo cell of a specified
precision is by default associated with any less precise geo cells
that contain the geo cell. For example, if a signal is associated
with a geo cell of precision 9, the signal is by default also
associated with corresponding geo cells of precisions 1, 2, 3, 4,
5, 6, 7, and 8. Similar mechanisms are applicable to other tiling
and geo cell arrangements. For example, S2 has a cell level
hierarchy ranging from level zero (85,011,012 km.sup.2) to level 30
(between 0.48 cm.sup.2 to 0.96 cm.sup.2).
[0062] Signal Ingestion and Normalization
[0063] Signal ingestion modules ingest a variety of raw structured
and/or raw unstructured signals on an on going basis and in
essentially real-time. Raw signals can include social posts, live
broadcasts, traffic camera feeds, other camera feeds (e.g., from
other public cameras or from CCTV cameras), listening device feeds,
911 calls, weather data, planned events, IoT device data, crowd
sourced traffic and road information, satellite data, air quality
sensor data, smart city sensor data, public radio communication
(e.g., among first responders and/or dispatchers, between air
traffic controllers and pilots), etc. The content of raw signals
can include images, video, audio, text, etc.
[0064] In general, signal normalization can prepare (or
pre-process) raw signals into normalized signals to increase
efficiency and effectiveness of subsequent computing activities,
such as, event detection, event notification, etc., that utilize
the normalized signals. For example, signal ingestion modules can
normalize raw signals into normalized signals having a Time,
Location, and Context (TLC) dimensions. An event detection
infrastructure can use the Time, Location, and Content dimensions
to more efficiently and effectively detect events.
[0065] Per signal type and signal content, different normalization
modules can be used to extract, derive, infer, etc. Time, Location,
and Context dimensions from/for a raw signal. For example, one set
of normalization modules can be configured to extract/derive/infer
Time, Location and Context dimensions from/for social signals.
Another set of normalization modules can be configured to
extract/derive/infer Time, Location and Context dimensions from/for
Web signals. A further set of normalization modules can be
configured to extract/derive/infer Time, Location and Context
dimensions from/for streaming signals.
[0066] Normalization modules for extracting/deriving/inferring
Time, Location, and Context dimensions can include text processing
modules, NLP modules, image processing modules, video processing
modules, etc. The modules can be used to extract/derive/infer data
representative of Time, Location, and Context dimensions for a
signal. Time, Location, and Context dimensions for a signal can be
extracted/derived/inferred from metadata and/or content of the
signal.
[0067] For example, NLP modules can analyze metadata and content of
a sound clip to identify a time, location, and keywords (e.g.,
fire, shooter, etc.). An acoustic listener can also interpret the
meaning of sounds in a sound clip (e.g., a gunshot, vehicle
collision, etc.) and convert to relevant context. Live acoustic
listeners can determine the distance and direction of a sound.
Similarly, image processing modules can analyze metadata and pixels
in an image to identify a time, location and keywords (e.g., fire,
shooter, etc.). Image processing modules can also interpret the
meaning of parts of an image (e.g., a person holding a gun, flames,
a store logo, etc.) and convert to relevant context. Other modules
can perform similar operations for other types of content including
text and video.
[0068] Per signal type, each set of normalization modules can
differ but may include at least some similar modules or may share
some common modules. For example, similar (or the same) image
analysis modules can be used to extract named entities from social
signal images and public camera feeds. Likewise, similar (or the
same) NLP modules can be used to extract named entities from social
signal text and web text.
[0069] In some aspects, an ingested signal includes sufficient
expressly defined time, location, and context information upon
ingestion. The expressly defined time, location, and context
information is used to determine Time, Location, and Context
dimensions for the ingested signal. In other aspects, an ingested
signal lacks expressly defined location information or expressly
defined location information is insufficient (e.g., lacks
precision) upon ingestion. In these other aspects, Location
dimension or additional Location dimension can be inferred from
features of an ingested signal and/or through references to other
data sources. In further aspects, an ingested signal lacks
expressly defined context information or expressly defined context
information is insufficient (e.g., lacks precision) upon ingestion.
In these further aspects, Context dimension or additional Context
dimension can be inferred from features of an ingested signal
and/or through reference to other data sources.
[0070] In further aspects, time information may not be included, or
included time information may not be given with high enough
precision and Time dimension is inferred. For example, a user may
post an image to a social network which had been taken some
indeterminate time earlier.
[0071] Normalization modules can use named entity recognition and
reference to a geo cell database to infer Location dimension. Named
entities can be recognized in text, images, video, audio, or sensor
data. The recognized named entities can be compared to named
entities in geo cell entries. Matches indicate possible signal
origination in a geographic area defined by a geo cell.
[0072] As such, a normalized signal can include a Time dimension, a
Location dimension, a Context dimension (e.g., single source
probabilities and probability details), a signal type, a signal
source, and content.
[0073] A single source probability can be calculated by single
source classifiers (e.g., machine learning models, artificial
intelligence, neural networks, statistical models, etc.) that
consider hundreds, thousands, or even more signal features of a
signal. Single source classifiers can be based on binary models
and/or multi-class models.
[0074] FIG. 1A depicts part of computer architecture 100 that
facilitates ingesting and normalizing signals. As depicted,
computer architecture 100 includes signal ingestion modules 101 and
raw signals 121, including social signals 171, Web signals 172, and
streaming signals 173. Raw signals 121 can also include other
signal types, including database signals. Raw signals 121,
including signal ingestion modules 101, social signals 171, Web
signals 172, streaming signals 173, and other signal types can be
connected to (or be part of) a network, such as, for example, a
system bus, a Local Area Network ("LAN"), a Wide Area Network
("WAN"), and even the Internet. Accordingly, raw signals 121,
including signal ingestion modules 101, social signals 171, Web
signals 172, streaming signals 173, and other signal types as well
as any other connected computer systems and their components can
create and exchange message related data (e.g., Internet Protocol
("IP") datagrams and other higher layer protocols that utilize IP
datagrams, such as, Transmission Control Protocol ("TCP"),
Hypertext Transfer Protocol ("HTTP"), Simple Mail Transfer Protocol
("SMTP"), Simple Object Access Protocol (SOAP), etc. or using other
non-datagram protocols) over the network.
[0075] Signal ingestion module(s) 101 can ingest raw signals 121,
including social signals 171, web signals 172, streaming signals
173, and other signal types (e.g., social posts, traffic camera
feeds, other camera feeds, listening device feeds, 911 calls,
weather data, planned events, IoT device data, crowd sourced
traffic and road information, satellite data, air quality sensor
data, smart city sensor data, public radio communication, database
data, etc.) on an on going basis and in essentially real-time.
Signal ingestion module(s) 101 include social content ingestion
modules 174, web content ingestion modules 175, stream content
ingestion modules 176, and signal formatter 180. Signal formatter
180 further includes social signal processing module 181, web
signal processing module 182, and stream signal processing modules
183.
[0076] For each type of signal, a corresponding ingestion module
and signal processing module can interoperate to normalize the
signal into a Time, Location, Context (TLC) dimensions. For
example, social content ingestion modules 174 and social signal
processing module 181 can interoperate to normalize social signals
171 into TLC dimensions. Similarly, web content ingestion modules
175 and web signal processing module 182 can interoperate to
normalize web signals 172 into TLC dimensions. Likewise, stream
content ingestion modules 176 and stream signal processing modules
183 can interoperate to normalize streaming signals 173 into TLC
dimensions.
[0077] In one aspect, signal ingestion module(s) 101 also include
ingestion and processing modules for other signal types, such as,
for example, database signals. The database ingestion modules and
database processing modules can interoperate to normalize database
signals.
[0078] In one aspect, signal content exceeding specified size
requirements (e.g., audio or video) is cached upon ingestion.
Signal ingestion modules 101 include a URL or other identifier to
the cached content within the context for the signal.
[0079] In one aspect, signal formatter 180 includes modules for
determining a single source probability as a ratio of signals
turning into events based on the following signal properties: (1)
event class (e.g., fire, accident, weather, etc.), (2) media type
(e.g., text, image, audio, etc.), (3) source (e.g., twitter,
traffic camera, first responder radio traffic, etc.), and (4) geo
type (e.g., geo cell, region, or non-geo). Probabilities can be
stored in a lookup table for different combinations of the signal
properties. Features of a signal can be derived and used to query
the lookup table. For example, the lookup table can be queried with
terms ("accident", "image", "twitter", "region"). The corresponding
ratio (probability) can be returned from the table.
[0080] In another aspect, signal formatter 180 includes a plurality
of single source classifiers (e.g., artificial intelligence,
machine learning modules, neural networks, etc.). Each single
source classifier can consider hundreds, thousands, or even more
signal features of a signal. Signal features of a signal can be
derived and submitted to a signal source classifier. The single
source classifier can return a probability that a signal indicates
a type of event. Single source classifiers can be binary
classifiers or multi-source classifiers.
[0081] Raw classifier output can be adjusted to more accurately
represent a probability that a signal is a "true positive". For
example, 1,000 signals whose raw classifier output is 0.9 may
include 80% as true positives. Thus, probability can be adjusted to
0.8 to reflect true probability of the signal being a true
positive. "Calibration" can be done in such a way that for any
"calibrated score" this score reflects the true probability of a
true positive outcome.
[0082] Signal ingestion modules 101 can insert one or more single
source probabilities and corresponding probability details into a
normalized signal to represent a Context (C) dimension. Probability
details can indicate a probabilistic model and features used to
calculate the probability. In one aspect, a probabilistic model and
signal features are contained in a hash field.
[0083] Signal ingestion modules 101 can access
"transdimensionality" transformations structured and defined in a
"TLC" dimensional model. Signal ingestion modules 101 can apply the
"transdimensionality" transformations to generic source data in raw
signals to re-encode the source data into normalized data having
lower dimensionality. Dimensionality reduction can include reducing
dimensionality of a raw signal to a normalized signal including a T
vector, an L vector, and a C vector. At lower dimensionality, the
complexity of measuring "distances" between dimensional vectors
across different normalized signals is reduced.
[0084] Thus, in general, any received raw signals can be normalized
into normalized signals including a Time (T) dimension, a Location
(L) dimension, a Context (C) dimension, signal source, signal type,
and content. Signal ingestion modules 101 can send normalized
signals 122 to event detection infrastructure 103.
[0085] For example, signal ingestion modules 101 can send
normalized signal 122A, including time 123A, location 124A, context
126A, content 127A, type 128A, and source 129A to event detection
infrastructure 103. Similarly, signal ingestion modules 101 can
send normalized signal 122B, including time 123B, location 124B,
context 126B, content 127B, type 128B, and source 129B to event
detection infrastructure 103.
[0086] Event Detection
[0087] FIG. 1B depicts part of computer architecture 100 that
facilitates detecting events. As depicted, computer architecture
100 includes geo cell database 111 and even notification 116. Geo
cell database 111 and event notification 116 can be connected to
(or be part of) a network with signal ingestion modules 101 and
event detection infrastructure 103. As such, geo cell database 111
and even notification 116 can create and exchange message related
data over the network.
[0088] As described, in general, on an ongoing basis, concurrently
with signal ingestion (and also essentially in real-time), event
detection infrastructure 103 detects different categories of
(planned and unplanned) events (e.g., fire, police response, mass
shooting, traffic accident, natural disaster, storm, active
shooter, concerts, protests, etc.) in different locations (e.g.,
anywhere across a geographic area, such as, the United States, a
State, a defined area, an impacted area, an area defined by a geo
cell, an address, etc.), at different times from Time, Location,
and Context dimensions included in normalized signals. Since,
normalized signals are normalized to include Time, Location, and
Context dimensions, event detection infrastructure 103 can handle
normalized signals in a more uniform manner increasing event
detection efficiency and effectiveness.
[0089] Event detection infrastructure 103 can also determine an
event truthfulness, event severity, and an associated geo cell. In
one aspect, a Context dimension in a normalized signal increases
the efficiency and effectiveness of determining truthfulness,
severity, and an associated geo cell.
[0090] Generally, an event truthfulness indicates how likely a
detected event is actually an event (vs. a hoax, fake,
misinterpreted, etc.). Truthfulness can range from less likely to
be true to more likely to be true. In one aspect, truthfulness is
represented as a numerical value, such as, for example, from 1
(less truthful) to 10 (more truthful) or as percentage value in a
percentage range, such as, for example, from 0% (less truthful) to
100% (more truthful). Other truthfulness representations are also
possible. For example, truthfulness can be a dimension or
represented by one or more vectors.
[0091] Generally, an event severity indicates how severe an event
is (e.g., what degree of badness, what degree of damage, etc. is
associated with the event). Severity can range from less severe
(e.g., a single vehicle accident without injuries) to more severe
(e.g., multi vehicle accident with multiple injuries and a possible
fatality). As another example, a shooting event can also range from
less severe (e.g., one victim without life threatening injuries) to
more severe (e.g., multiple injuries and multiple fatalities). In
one aspect, severity is represented as a numerical value, such as,
for example, from 1 (less severe) to 5 (more severe). Other
severity representations are also possible. For example, severity
can be a dimension or represented by one or more vectors.
[0092] In general, event detection infrastructure 103 can include a
geo determination module including modules for processing different
kinds of content including location, time, context, text, images,
audio, and video into search terms. The geo determination module
can query a geo cell database with search terms formulated from
normalized signal content. The geo cell database can return any geo
cells having matching supplemental information. For example, if a
search term includes a street name, a subset of one or more geo
cells including the street name in supplemental information can be
returned to the event detection infrastructure.
[0093] Event detection infrastructure 103 can use the subset of geo
cells to determine a geo cell associated with an event location.
Events associated with a geo cell can be stored back into an entry
for the geo cell in the geo cell database. Thus, over time an
historical progression of events within a geo cell can be
accumulated.
[0094] As such, event detection infrastructure 103 can assign an
event ID, an event time, an event location, an event category, an
event description, an event truthfulness, and an event severity to
each detected event. Detected events can be sent to relevant
entities, including to mobile devices, to computer systems, to
APIs, to data storage, etc.
[0095] Event detection infrastructure 103 detects events from
information contained in normalized signals 122. Event detection
infrastructure 103 can detect an event from a single normalized
signal 122 or from multiple normalized signals 122. In one aspect,
event detection infrastructure 103 detects an event based on
information contained in one or more normalized signals 122. In
another aspect, event detection infrastructure 103 detects a
possible event based on information contained in one or more
normalized signals 122. Event detection infrastructure 103 then
validates the potential event as an event based on information
contained in one or more other normalized signals 122.
[0096] As depicted, event detection infrastructure 103 includes geo
determination module 104, categorization module 106, truthfulness
determination module 107, and severity determination module
108.
[0097] Geo determination module 104 can include NLP modules, image
analysis modules, etc. for identifying location information from a
normalized signal. Geo determination module 104 can formulate
(e.g., location) search terms 141 by using NLP modules to process
audio, using image analysis modules to process images, etc. Search
terms can include street addresses, building names, landmark names,
location names, school names, image fingerprints, etc. Event
detection infrastructure 103 can use a URL or identifier to access
cached content when appropriate.
[0098] Categorization module 106 can categorize a detected event
into one of a plurality of different categories (e.g., fire, police
response, mass shooting, traffic accident, natural disaster, storm,
active shooter, concerts, protests, etc.) based on the content of
normalized signals used to detect and/or otherwise related to an
event.
[0099] Truthfulness determination module 107 can determine the
truthfulness of a detected event based on one or more of: source,
type, age, and content of normalized signals used to detect and/or
otherwise related to the event. Some signal types may be inherently
more reliable than other signal types. For example, video from a
live traffic camera feed may be more reliable than text in a social
media post. Some signal sources may be inherently more reliable
than others. For example, a social media account of a government
agency may be more reliable than a social media account of an
individual. The reliability of a signal can decay over time.
[0100] Severity determination module 108 can determine the severity
of a detected event based on or more of: location, content (e.g.,
dispatch codes, keywords, etc.), and volume of normalized signals
used to detect and/or otherwise related to an event. Events at some
locations may be inherently more severe than events at other
locations. For example, an event at a hospital is potentially more
severe than the same event at an abandoned warehouse. Event
category can also be considered when determining severity. For
example, an event categorized as a "Shooting" may be inherently
more severe than an event categorized as "Police Presence" since a
shooting implies that someone has been injured.
[0101] Geo cell database 111 includes a plurality of geo cell
entries. Each geo cell entry is included in a geo cell defining an
area and corresponding supplemental information about things
included in the defined area. The corresponding supplemental
information can include latitude/longitude, street names in the
area defined by and/or beyond the geo cell, businesses in the area
defined by the geo cell, other Areas of Interest (AOIs) (e.g.,
event venues, such as, arenas, stadiums, theaters, concert halls,
etc.) in the area defined by the geo cell, image fingerprints
derived from images captured in the area defined by the geo cell,
and prior events that have occurred in the area defined by the geo
cell. For example, geo cell entry 151 includes geo cell 152,
lat/lon 153, streets 154, businesses 155, AOIs 156, and prior
events 157. Each event in prior events 157 can include a location
(e.g., a street address), a time (event occurrence time), an event
category, an event truthfulness, an event severity, and an event
description. Similarly, geo cell entry 161 includes geo cell 162,
lat/lon 163, streets 164, businesses 165, AOIs 166, and prior
events 167. Each event in prior events 167 can include a location
(e.g., a street address), a time (event occurrence time), an event
category, an event truthfulness, an event severity, and an event
description.
[0102] Other geo cell entries can include the same or different
(more or less) supplemental information, for example, depending on
infrastructure density in an area. For example, a geo cell entry
for an urban area can contain more diverse supplemental information
than a geo cell entry for an agricultural area (e.g., in an empty
field).
[0103] Geo cell database 111 can store geo cell entries in a
hierarchical arrangement based on geo cell precision. As such, geo
cell information of more precise geo cells is included in the geo
cell information for any less precise geo cells that include the
more precise geo cell.
[0104] Geo determination module 104 can query geo cell database 111
with search terms 141. Geo cell database 111 can identify any geo
cells having supplemental information that matches search terms
141. For example, if search terms 141 include a street address and
a business name, geo cell database 111 can identify geo cells
having the street name and business name in the area defined by the
geo cell. Geo cell database 111 can return any identified geo cells
to geo determination module 104 in geo cell subset 142.
[0105] Geo determination module can use geo cell subset 142 to
determine the location of event 135 and/or a geo cell associated
with event 135. As depicted, event 135 includes event ID 132, time
133, location 137, description 136, category 137, truthfulness 138,
and severity 139.
[0106] Event detection infrastructure 103 can also determine that
event 135 occurred in an area defined by geo cell 162 (e.g., a
geohash having precision of level 7 or level 9). For example, event
detection infrastructure 103 can determine that location 134 is in
the area defined by geo cell 162. As such, event detection
infrastructure 103 can store event 135 in events 167 (i.e.,
historical events that have occurred in the area defined by geo
cell 162).
[0107] Event detection infrastructure 103 can also send event 135
to event notification module 116. Event notification module 116 can
notify one or more entities about event 135.
[0108] FIG. 2 illustrates a flow chart of an example method 200 for
normalizing ingested signals. Method 200 will be described with
respect to the components and data in computer architecture
100.
[0109] Method 200 includes ingesting a raw signal including a time
stamp, an indication of a signal type, an indication of a signal
source, and content (201). For example, signal ingestion modules
101 can ingest a raw signal 121 from one of: social signals 171,
web signals 172, or streaming signals 173.
[0110] Method 200 includes forming a normalized signal from
characteristics of the raw signal (202). For example, signal
ingestion modules 101 can form a normalized signal 122A from the
ingested raw signal 121.
[0111] Forming a normalized signal includes forwarding the raw
signal to ingestion modules matched to the signal type and/or the
signal source (203). For example, if ingested raw signal 121 is
from social signals 171, raw signal 121 can be forwarded to social
content ingestion modules 174 and social signal processing modules
181. If ingested raw signal 121 is from web signals 172, raw signal
121 can be forwarded to web content ingestion modules 175 and web
signal processing modules 182. If ingested raw signal 121 is from
streaming signals 173, raw signal 121 can be forwarded to stream
content ingestion modules 176 and streaming signal processing
modules 183.
[0112] Forming a normalized signal includes determining a time
dimension associated with the raw signal from the time stamp (204).
For example, signal ingestion modules 101 can determine time 123A
from a time stamp in ingested raw signal 121.
[0113] Forming a normalized signal includes determining a location
dimension associated with the raw signal from one or more of:
location information included in the raw signal or from location
annotations inferred from signal characteristics (205). For
example, signal ingestion modules 101 can determine location 124A
from location information included in raw signal 121 or from
location annotations derived from characteristics of raw signal 121
(e.g., signal source, signal type, signal content).
[0114] Forming a normalized signal includes determining a context
dimension associated with the raw signal from one or more of:
context information included in the raw signal or from context
signal annotations inferred from signal characteristics (206). For
example, signal ingestion modules 101 can determine context 126A
from context information included in raw signal 121 or from context
annotations derived from characteristics of raw signal 121 (e.g.,
signal source, signal type, signal content).
[0115] Forming a normalized signal includes inserting the time
dimension, the location dimension, and the context dimension in the
normalized signal (207). For example, signal ingestion modules 101
can insert time 123A, location 124A, and context 126A in normalized
signal 122. Method 200 includes sending the normalized signal to an
event detection infrastructure (208). For example, signal ingestion
modules 101 can send normalized signal 122A to event detection
infrastructure 103.
[0116] Communication Channel Event Detection System
[0117] A system for detecting events from communication channels
can include one or more communication channels and one or more
analysis modules. A communication channel can carry one or more
(e.g., streaming) communication signals, such as, streaming audio
signals. Events can be detected from characteristics of
communication signals ingested from the one or more communication
channels.
[0118] Each communication channel can pre-associated with a
geographic region or jurisdiction, such as, a municipality, county,
city block, district, city, state, or other geographic region, but
can alternatively be associated with a plurality of geographic
regions. The geographic region for a communication channel can be
received from a user, but can alternatively be automatically
learned (e.g., based on the locations extracted from a
communication signal) or otherwise determined. A communication
channel can be an audio chancel, a video channel, or any other
suitable channel. A communication channel can be a radio channel
(e.g., a predetermined set of radio frequencies), optical channel
(e.g., a predetermined set of light frequencies), or any other
suitable channel. Examples of communication channels include:
wireline and wireless telephone networks, computer networks
broadcast and cable television, radio, Public Safety Land Mobile
Radio, satellite systems, the Internet, Public Safety Answer Points
(PSAPs) networks, Voice over Internet Protocol (VoIP) networks, or
any other suitable channel. A communication channel can be public
or private (e.g., wherein the login information can be stored,
tokenized, or otherwise processed). Communication channels can be
emergency communications (e.g., police, medical, coast guard, snow
rescue, fire rescue, etc.), fleet communications (e.g., for a
ridesharing fleet, a trucking fleet, etc.), or any other suitable
set of communications.
[0119] A communication signal can encode content or other
information. A communication signal can be an audio signal, video
signal, or any other suitable signal. A communication signal can be
digital (e.g., VOIP), analog, or have any other suitable format. A
communication signal can be associated with signal parameters
(e.g., audio parameters), such as amplitude (e.g., volume),
frequency, patterns, or any other suitable parameters. A
communication signal can be generated by an operator (e.g.,
dispatcher), in-field personnel (e.g., emergency personnel,
firefighters, fleet operators, police, etc.), automatically
generated, or generated by any other suitable entity. The entities
can be associated with schedules (e.g., shifts), voice
fingerprints, identifiers (e.g., names, codes, etc.), communication
channels, modules (e.g., syntax modules), geographic regions (e.g.,
city blocks, municipalities, etc.), or any other suitable
information. The entity information can be received (e.g., from a
managing entity, such as a manager), automatically learned (e.g.,
historic patterns extracted from historic communication signals for
the communication channel), or otherwise determined.
[0120] The one or more analysis modules can analyze communication
signals or subsets thereof (e.g., communication clips,
communication sub-clips, etc.). For example, a first module can
select communication clips from the communication signal, a second
module can identify a sub-clip for event probability analysis, a
third module can determine the event probability from the sub-clip,
a fourth module can determine a clip score for the communication
clip (e.g., based on the sub-clip, surrounding signals, signal
parameters, etc.), a fifth module can extract event parameters from
the communication clip, and a sixth module can determine an event
score based on the extracted event parameters. However, the system
can include any suitable number of modules, arranged in any
suitable configuration. The analysis modules can be included in,
integrated with, and/or interoperate with signal ingestion modules
101 (e.g., stream content ingestion modules 176 and stream
processing modules 183) and/or event detection infrastructure
103.
[0121] Analysis modules can be specific to a communication channel,
a subset of communication channels (e.g., sharing a common
parameter, such as location, state, time, etc.), an analysis stage,
a set of analysis stages, be global, or be otherwise related to
analysis processes and/or communication channels. For example, a
clip determination module can be used for communication signals
from any communication channel (e.g., wherein the signals can be
fed into the same clip determination module, or the same clip
determination module is replicated for each communication channel)
with channel-specific modules used for remaining processes.
However, analysis modules can be shared across channels for any
suitable process. Analysis modules can be constant over time (e.g.,
used in multiple contexts), or can be dynamically selected based on
time of day, event type (e.g., selected based on the event
identifier extracted from the communication clip), the entities
generating the communication signal, or based on any other suitable
contextual parameter.
[0122] One or analysis more modules with the same communication
channel and/or analysis process can operate concurrently. For
example, multiple analysis modules can be concurrently executed to
minimize module overfitting, and/or to concurrently test multiple
versions of the same module (e.g., wherein the best module, such as
the module that generates the least number of false positives or
negatives, is subsequently selected for general use or subsequent
training). The multiple modules can process the same clips,
different clips, or any suitable set of clips. In another example,
multiple modules are serially tested (e.g., on serial clips, the
same clips, etc.). However, any suitable number of module variants
can be used.
[0123] Each analysis module can implement any one or more of: a
rule-based system (e.g., heuristic system), fuzzy logic (e.g.,
fuzzy matching), regression systems (e.g., ordinary least squares,
logistic regression, stepwise regression, multivariate adaptive
regression splines, locally estimated scatterplot smoothing, etc.),
genetic programs, support vectors, an instance-based method (e.g.,
k-nearest neighbor, learning vector quantization, self-organizing
map, etc.), a regularization method (e.g., ridge regression, least
absolute shrinkage and selection operator, elastic net, etc.), a
decision tree learning method (e.g., classification and regression
tree, iterative dichotomiser 3, C4.5, chi-squared automatic
interaction detection, decision stump, random forest, multivariate
adaptive regression splines, gradient boosting machines, etc.), a
Bayesian method (e.g., naive Bayes, averaged one-dependence
estimators, Bayesian belief network, etc.), a kernel method (e.g.,
a support vector machine, a radial basis function, a linear
discriminate analysis, etc.), a clustering method (e.g., k-means
clustering, expectation maximization, etc.), an associated rule
learning algorithm (e.g., an Apriori algorithm, an Eclat algorithm,
etc.), an artificial neural network model (e.g., a Perceptron
method, a back-propagation method, a Hopfield network method, a
self-organizing map method, a learning vector quantization method,
etc.), a deep learning algorithm (e.g., a restricted Boltzmann
machine, a deep belief network method, a convolution network
method, a stacked auto-encoder method, etc.), a dimensionality
reduction method (e.g., principal component analysis, partial lest
squares regression, Sammon mapping, multidimensional scaling,
projection pursuit, etc.), an ensemble method (e.g., boosting,
bootstrapped aggregation, AdaBoost, stacked generalization,
gradient boosting machine method, random forest method, etc.),
and/or any suitable form of machine learning algorithm. Each module
can additionally or alternatively include: probabilistic
properties, heuristic properties, deterministic properties, and/or
any other suitable properties. However, analysis modules can
leverage any suitable computation method, machine learning method,
and/or combination thereof.
[0124] Each analysis module can be generated, trained, updated, or
otherwise adjusted using one or more of: manual generation (e.g.,
received from a user), supervised learning (e.g., using logistic
regression, using back propagation neural networks, using random
forests, decision trees, etc.), unsupervised learning (e.g., using
an Apriori algorithm, using K-means clustering), semi-supervised
learning, reinforcement learning (e.g., using a Q-learning
algorithm, using temporal difference learning), and any other
suitable learning style.
[0125] Each analysis module can be validated, verified, reinforced,
calibrated, or otherwise updated based on newly received,
up-to-date signals; historic signals (e.g., labeled or unlabeled);
or be updated based on any other suitable data. Each module can be
run or updated: in response to a number of false positives and/or
false negatives exceeding a threshold value, determination of an
actual result differing from an expected result, or at any other
suitable frequency.
[0126] Detecting Events from Communication Signals
[0127] Aspects of the invention include using the described system
to detect events from (e.g., streaming) communication signals,
including streaming audio signals. Events can be detected
concurrently as new communication signals are received, at a
predetermined frequency, sporadically, in response to a trigger
condition being met, or at any other suitable time.
[0128] FIG. 3 illustrates a flow chart of an example method 300 for
ingesting a communication stream and detecting an event. Method 300
includes receiving a communication stream (301). For example, a
communication stream can be received from an audio channel, a video
channel, or any other suitable channel. The communication stream
can be received by a remote computing system (e.g., server system),
a user device or any other suitable device. The communication
stream can be generated by an entity, automatically generated
(e.g., by a burglar alarm system), or be otherwise generated. A
plurality of communication streams can be received concurrently
from one or more communication channels or can be received at
different times (e.g., in response to occurrence of a receipt
condition being met, such as the signal amplitude exceeding a
threshold value for a threshold period of time, etc.).
[0129] Method 300 includes selecting a communication clip from the
communication stream (302). For example, a communication clip can
be an audio clip, a video clip, or any other suitable clip. A
communication clip can be a time-bounded segment of the received
communication stream (e.g., substantially continuous segment,
etc.), but can be otherwise defined. The communication clip can
encompass a statement by a single entity, a conversation between
two or more entities, or include any other suitable set of
communications. One or more communication clips can be identified
from a communication stream. The communication clip can be
associated with a beginning timestamp, an end timestamp, a
duration, one or more entities (e.g., generating the content in the
clip), location (e.g., geographic region associated with the
communication channel, geographic sub-region identified from the
communication content, etc.), event, and/or any other suitable
parameter.
[0130] As such, identifying the communication clip can include
determining a clip beginning timestamp, determining a clip end
timestamp, and storing a communication segment between the
beginning and end timestamps. The beginning and end timestamps can
be determined by a beginning module and end module, respectively.
The beginning and end modules can be shared across a plurality of
communication channels (e.g., be global modules) or can be specific
to a given communication channel. The communication stream can be
pre-processed to remove noise or other undesired acoustic features
prior to communication clip identification, or can be the raw
communication stream, a compressed communication stream, a
normalized communication stream, or any other suitable version of
the communication stream.
[0131] Determining a clip beginning timestamp can include detecting
a beginning condition in the communication stream (e.g., received
from a user, triggered by a sensor measurement, etc.). The
beginning condition can include: the signal amplitude exceeding a
threshold amplitude for a threshold period of time (e.g., a
sustained increased volume on the channel; wherein the signal can
be cached for at least the threshold period of time to enable
beginning condition detection), the signal amplitude change
exceeding a threshold change (e.g., a sudden increase in volume on
the channel), the signal frequency patterns substantially matching
a predetermined pattern (e.g., a characteristic beep or keyword
precedes every transmission, wherein the pattern is associated with
the beep or keyword), or be any other suitable condition.
[0132] Determining a clip end timestamp can include detecting an
ending condition in the communication stream (e.g., received from a
user, triggered by a sensor measurement, etc.). The ending
condition can include: a satisfaction of a time condition after the
beginning condition was detected (e.g., 30 seconds after the
beginning condition, 1 minute after the beginning condition, etc.);
the signal amplitude or difference falling below a threshold
amplitude for a threshold period of time (e.g., wherein the last
speech timestamp before the delay is set as the end timestamp); the
signal frequency patterns substantially matching a predetermined
pattern (e.g., a characteristic beep or keyword marks the end of
every transmission, wherein the pattern is associated with the beep
or keyword), or be any other suitable condition.
[0133] Identifying the communication clip can include stitching
multiple audio clips together into a conversation (e.g., between
dispatcher and emergency personnel). Stitching can be performed
after a highly scored clip is detected, after a beginning condition
is detected, or at any suitable time. In one variation, stitching
includes: identifying a first voice and keyword within a first
communication clip, identifying a second voice and the same or
related keyword within a second communication clip, identifying
subsequent audio clips including the first and second voices until
a conversation end condition is met (e.g., end keyword is detected,
the first or second voice has not been detected for a threshold
period of time, etc.), and stitching the identified clips together
into a conversation clip, wherein the conversation clip can be
subsequently analyzed as the communication clip. However, the
conversation can be otherwise aggregated.
[0134] Storing a communication segment between a beginning and end
timestamps can include caching the communication stream after the
beginning timestamp (e.g., timestamp of the stream segment from
which the beginning condition was detected) until the end timestamp
is determined (e.g., until the end condition is met), wherein
segments of the communication stream outside of the beginning and
end timestamps are not stored and/or deleted immediately.
Alternatively, storing the communication segment can include
storing the entire communication stream, post-processing the
communication stream to extract communication segments, and
optionally deleting the source communication stream. However, the
communication segment can be otherwise stored.
[0135] Method 300 includes determining a clip score for the
communication clip (303). For example, a clip score can be computed
for a stored (cached) communication segment. Based on a clip score,
it can be determined if further processing is worthwhile (e.g., how
likely a clip is to include event information). Thus, a clip score
can be indicative of whether a communication clip includes event
information, more preferably whether the communication clip
includes information for an event of interest, but can be
associated with any other suitable parameter. An event of interest
can be an event that occurs with less than a threshold frequency
within a geographic region, be an event having a classification or
type falling within a predetermined list (e.g., automatically
generated, received from a managing entity, etc.), be an event with
an event score exceeding a threshold score, or be otherwise
defined.
[0136] A clip score can be: a score, a classification, a
probability, a fitness, or be any other suitable metric. The clip
score can be determined by a clip score module, such as a
heuristic, probabilistic, or Bayesian module, a neural network, a
genetic program, or other suitable module leveraging any other
suitable analysis process. A clip score module can be specific to a
communication channel (and/or entity generating the communication
signal, such as a dispatcher) or can be global.
[0137] In one aspect, determining a clip score includes (a)
identifying a sub-clip within the communication clip with the
highest probability of having an event identifier, (b) analyzing
the sub-clip for an event identifier, and (c) determining the clip
score based on event identifier. The event identifier can be a
keyword (e.g., "fire," "shot," other descriptor, etc.), a code
(e.g., "211" for robbery, "242" for battery, "187" for homicide,
etc.), or be any other suitable identifier.
[0138] A sub-clip can be identified by a module specific to a
communication channel or geographic region associated with the
communication channel. A sub-clip can be identified (e.g.,
extracted) based on a predetermined syntax or pattern associated
with the communication channel or geographic region randomly
selected, determined by pattern-matching the signal parameters
(e.g., amplitude patterns, frequency patterns, etc.), or otherwise
identified. A predetermined syntax can include an event identifier
position within the communication clip and can include other
information arranged in a predetermined pattern or order.
[0139] For example, a predetermined syntax can include an event
identifier, followed by an event severity, followed by an event
location. In another example, a predetermined syntax can include an
entity identifier, an event identifier, and an event description.
In a further example, a predetermined syntax can include an event
identifier's start and end timestamp within the communication clip
(e.g., between 0 sec and 30 sec, between 10 sec and 30 sec into the
clip). A predetermined syntax can be received from an entity
associated with the communication channel or geographic region,
learned (e.g., from historic communication clips from the
communication channel), or otherwise determined.
[0140] FIG. 6A depicts an example channel syntax 601 including an
event identifier, followed by a recipient ID, followed by an event
location, followed by event details. FIG. 6B depicts an example
channel syntax 602 including an event identifier, followed by
another event identifier, followed by a recipient ID, followed by
an event location.
[0141] Analyzing a sub-clip for an event identifier can determine
whether the clip includes an event identifier and/or which event
identifier the clip includes. In a first variation, analyzing a
sub-clip includes: performing natural language processing on the
sub-clip to extract the event identifier. In a second variation,
analyzing the sub-clip includes: identifying the author of the
sub-clip (e.g., the speaker), retrieving signal patterns associated
with the author, identifying a signal pattern (from the retrieved
signal patterns) substantially matching the sub-clip, and
associating the sub-clip with the event identifier associated with
the identified signal pattern. The author of the sub-clip can be
identified using: voice recognition, based on the time (e.g.,
according to a shift schedule), an entity code or name extracted
from the communication clip, or otherwise determined.
[0142] Determining the clip score based on event identifier can
indicate whether a communication clip is to be subsequently
(further) processed to extract event parameters. In one variation,
a clip is assigned a clip score associated with the event
identifier (e.g., extracted from the sub-clip). In a second
variation, a clip score is calculated from the event identifier,
signal parameters (e.g., amplitude, frequency, etc.), keywords
surrounding the sub-clip, and/or any other suitable parameter
value.
[0143] In a second variation, a clip score is determined from
feature values extracted from the communication clip. In one
aspect, clips score determination includes: extracting keywords
from the communication clip (e.g., event identifiers, etc.) and
calculating the clip score based on values, weights, and/or other
metrics associated with the extracted keywords. In a second aspect,
clips score determination includes: identifying a location from the
communication clip, identifying social networking system content
authored within a predetermined time window of the communication
clip timestamp, and calculating the clip score based on features
extracted from the content.
[0144] In a third variation, a clip score is determined based on
the signal parameter values. For example, a clip score can be
calculated from the signal amplitude (e.g., mean, median, duration
above a threshold value, etc.), the amplitude change, the frequency
(e.g., mean, median, deviation, duration above a threshold value,
etc.), the signal parameter value deviation from a baseline value
for the geographic location, or from any other suitable signal
parameter value or metric. In a fourth variation, a clip score is
determined based on a signal pattern, wherein each of a
predetermined set of signal patterns is associated with a clip
score (e.g., determined from past verified events).
[0145] Method 300 includes extracting event parameters from the
communication clip (304). Event parameters can include the event
location, event time (e.g., start time, end time, duration),
subject matter or context (e.g., fire, shooting, homicide, cat in
tree, etc.), severity (e.g., 3 alarm fire, 2 alarm fire, gunfire v.
murder), people, or any other suitable event parameter. Event
parameters can be extracted from the communication clip in response
to clip score exceeding threshold clip score or upon satisfaction
of any other suitable condition. The event parameters can be
extracted from the entirety of a communication clip, from sub-clips
of the communication clip (e.g., the same or different sub-clip
from that used to determine the event identifier), from auxiliary
(other) signals identified based on the communication clip
parameters (e.g., geographic region, time, volume of interactions
between emergency personnel and operator, signal strength, signal
parameters, etc.), channel parameters (e.g., geographic region,
historic frequency of events detected from the channel), clip
content (e.g., words, phrases, cadence, number of back-and-forth
communications, etc.), or from any other suitable information.
Event parameters can be extracted using modules specific to a
communication channel (e.g., trained on historic labeled signals
from the communication channel), or using global modules. Each
event parameter can be extracted using a different module (e.g.,
one for event location, another for event time), but can
alternatively be extracted using the same module or any other
suitable set of modules.
[0146] In one variation, extracting event parameters includes
determining the event location. The event location can be a
geofence, specific location (e.g., latitude, longitude, etc.),
address, specific location obfuscated to a predetermined radius, a
geographic region identifier (e.g., municipality identifier,
district identifier, city identifier, state identifier, etc.), or
be any other suitable location identifier. The event location can
be determined from the geographic region associated with the source
communication channel, location identifiers extracted from the
communication clip, the location of the in-field recipient (e.g.,
retrieved from the recipient's user device location, the
recipient's social networking system posts, etc.), or determined
from any other suitable information.
[0147] In one aspect, determining event location can include:
identifying the initial geographic region associated with the
source communication channel, extracting a geographic identifier
(e.g., street names, address numbers, landmark names, etc.) from
the clip, finding a known geographic identifier within the initial
geographic region substantially matching the extracted geographic
identifier (e.g., best match), and assigning the known geographic
identifier as the event location. In another aspect, determining an
event location includes: extracting the geographic identifier from
the clip and searching a global set of known identifiers for a
match. Extracting the geographic identifier can include: analyzing
the entire communication clip for a geographic identifier (e.g.,
performing NLP on the entire clip); identifying a sub-clip with the
highest probability of having the geographic identifier (e.g.,
using the syntax model, trained on historic audio clips with the
geographic identifier portion tagged); identifying a signal pattern
associated with a geographic identifier and performing NLP on the
clip segment exhibiting the signal pattern; or otherwise extracting
the geographic identifier.
[0148] In a second variation, extracting event parameters includes
determining the event time. In a first example, event time can be
the communication clip time. In second example, event time can be a
time mentioned in the communication clip (e.g., specific time,
relative time, such as "5 minutes ago"). The time can be extracted
from a sub-clip with the highest probability of having the event
time, or otherwise determined.
[0149] In a third variation, extracting event parameters includes
determining the event subject matter or context. The subject matter
or context can be determined from the event identifier or from the
content surrounding the event identifier sub-clip (e.g., using NLP,
pattern matching, etc.).
[0150] Extracted event parameters can be used to compute time
dimension, location dimension, and context dimension for a (e.g.,
streaming) communication signal, such as, a streaming audio
signal.
[0151] In a fourth variation, extracting event parameters includes
determining an event severity. The event severity can be determined
from the event identifier (e.g., the event code, the keyword,
etc.), the voice register, the voice amplitude, the word frequency,
the keyword frequency, the word choice, or from any other suitable
parameter. The event severity can be a severity score, a
classification, or be any other suitable descriptor. For example,
the event severity can be higher when the voice register is higher
than the historic register for the entity. However, the event
severity can be otherwise determined.
[0152] Method 300 can include extracting event parameters from
auxiliary (other) signals (308). For example, event parameters can
be extracted from other signals received at signal ingestion
modules 101. Other signals can include social signals, web signals,
public cameras, social broadcasts, etc.
[0153] Method 300 includes determining an event score based on
event parameters (305). An event score can be determined using an
event identifier-specific module, a channel-specific module, a
context-specific module, a global module, or using any other
suitable module. An event score can be calculated, computed,
selected, matched, classified, or otherwise determined. An event
score can be determined based on: event parameters extracted from
the communication clip, the historic event parameters for the event
location (see FIG. 7), the historic event parameters for the
neighboring geographic locations, the event parameters extracted
from substantially concurrent communication signal(s) for
neighboring geographic locations, auxiliary signals associated with
the event location (e.g., posts from social networking systems,
video, etc.), the number of detected clips (or other signals)
associated with the event and/or event location, or from any other
suitable information.
[0154] In one example, an event score is calculated as a function
of the deviation between the extracted event parameter values and
the historic event parameter values. In a second example, an event
score is calculated from signal parameter values and an event
identifier, wherein the event identifier is associated with a value
and the signal parameters are associated with differing
weights.
[0155] FIG. 7 is an example of event scoring based on historic
parameter values for a geographic region. As depicted, (e.g.,
streaming) communication signals are received via channels 702A and
702B within geographic region 701. For each channel 702A and 702B,
a frequency per event class is depicted. For event IDs 703A and
703D indicating "homicide", lower event score 704A and higher event
score 704B can be computed.
[0156] In one aspect, an event score is computed from parameters
extracted from a (e.g., streaming) communication clip in
combination with parameters extracted from auxiliary (other
ingested) signals.
[0157] Method 300 includes detecting an event based on the event
score (306). An event can be detected in response to an event score
exceeding a threshold event score or upon satisfaction of any other
suitable condition. A threshold event score can specific to an
entity (e.g., financial entity, logistics entity, news entity,
brand entity, protection services, etc.), can be global or can have
any other suitable relevancy. A threshold event score can be
automatically determined for geographic region (e.g., based on
historic event scores for similar contexts, such as time; historic
clip scores for neighbors; etc.), received from the entity,
automatically selected based on entity parameters (e.g., the
entity's classification, profile, type, interests, keywords, event
interests, etc.), or otherwise determined.
[0158] An event can optionally be validated using auxiliary (other
ingested) signals. For example, a low-scoring event can be
considered an event of interest in response to the same event being
detected in multiple auxiliary (other) signal sources (e.g., social
networking systems, multiple emergency services, Web signals,
etc.). Alternatively, the event score can be augmented or otherwise
adjusted based on event scores extracted from the auxiliary (other
ingested) signals.
[0159] Method 300 includes acting based on the detected event
(307). For example, an action can be performed in response to event
detection. In a first variation, a notification is sent to an
entity in response to event detection. The notification can include
event parameters (e.g., extracted from the communication clip, from
auxiliary signals, etc.) or any other suitable information. In a
second variation, delivery systems, vehicles, or other systems are
automatically rerouted based on an event location, event type
(e.g., extracted from the event identifier), event severity, and/or
event time (e.g., to avoid the event). In a third variation,
content (e.g., posts, images, videos, sensor signals, or other
content) associated with the event location and generated proximal
the event time from the communication channel, auxiliary (other)
signals, or other sources is aggregated.
[0160] Event Score Generation Example
[0161] FIG. 4 illustrates an example computer architecture 400 that
facilitates generating an event score from ingested communication
streams. As depicted, computer architecture 400 includes clip
selector 401, clip score calculator 402, parameter extractor 404,
event score calculator 406, and resource allocator 407. Clip score
calculator 402 further includes sub-clip identifier 403. Clip
selector 401, clip score calculator 402, parameter extractor 404,
event score calculator 406, and resource allocator 407 can be
included in, integrated with, and/or interoperate with signal
ingestion modules 101 (e.g., stream content ingestion modules 176
and stream processing modules 183) and/or event detection
infrastructure 103.
[0162] In general, clip selector 401 is configured select a clip
from a (e.g., streaming) communication (e.g., audio) signal using
any of the described clip selection mechanisms. Clip score
calculator 402 is configured to calculate a clip score for a
selected clip using any of the described mechanisms. Sub-clip
identifier 403 is configured to identify a sub-clip from within a
selected clip using any of the described mechanisms. Parameter
extractor 404 is configured to extract parameters from a selected
clip using any of the described mechanisms. Event score calculator
is configured to calculate an event score from extracted parameters
using any of the described mechanisms.
[0163] Resource allocator 407 is configured to determine when
additional resources are to be allocated to further process a
selected clip. When additional resources are to be allocated,
resource allocator allocates the additional resources to parameter
extractor 404 and event score calculator. In one aspect, resource
allocator determines when additional resources are to be allocated
based on a clip score. When a clip score exceeds a threshold clip
score, additional resources can be allocated. When a clip score
does not exceed a threshold clip score, additional resources are
not allocated. As such, additional resources can be allocated when
there is increased likelihood of a (e.g., streaming) communication
signal including event information. Otherwise, resources are
conserved to allocate for other functions.
[0164] FIG. 5 illustrates a flow chart of an example method 500 for
generating an event score from an ingested communication stream.
Method 500 will be described with respect to the components and
data depicted in computer architecture 400.
[0165] Method 500 includes ingesting a communication stream (501).
For example, clip selector 401 can ingest communication signal 421
(e.g., a streaming audio signal) from audio signals 173A. Method
500 includes selecting a communication clip from within the
communication stream (502). For example, clip selector 401 can
select clip 422 from communication signal 421.
[0166] Method 500 includes computing a clip score from
characteristics of the communication clip that indicates a
likelihood of the communication stream including event information
(503). For example, clip score calculator 402 can compute clip
score 426 from characteristics of clip 422. Clip score 422
indicates a likelihood of communication signal 421 including event
information. In one aspect, sub-clip identifier 403 identifies a
sub-clip within the clip 422 with the highest probability of having
an event identifier. Clip score calculator 402 analyzes the
sub-clip for an event identifier and computes clip score 426 based
on the event identifier.
[0167] Method 500 includes determining further processing of the
communication clip is warranted based on the clip score (504). For
example, resource allocator 407 can determine that further
processing of communication signal 422 is warranted based on clip
score 426 (e.g., exceeding a threshold clip score). Method 500
includes allocating computing resources to further process the
communication clip (505). For example, resource allocator 407 can
allocate computing resources (e.g., memory resources, processing
resources, etc.) to parameter extractor 404 and event score
calculator 406
[0168] Method 500 includes extracting event parameters from the
communication clip utilizing the allocated computing resources
(506). For example, parameter extractor 404 extracts parameters 423
from clip 422 utilizing computing resources 427. Method 500
includes computing an event score from the extracted event
parameters (507). For example, event score calculator 406 can
calculate event score 424 from parameters 423 (utilizing computing
resources 427). Event score calculator can be sent to other modules
of event detection infrastructure 103.
[0169] Method 500 includes detecting an event based on the event
score (508). For example, event detection infrastructure 103 can
detect an event based on events score 424. Entities can be notified
of the detected event. The detected event can also provide a basis
for rerouting delivery vehicles or performing other actions.
[0170] Concurrent Handling of Signals
[0171] FIG. 8 a computer architecture 800 that facilitates
concurrently handling communication signals (or other signals) from
a plurality of channels. As depicted, computer architecture 800
includes clip extraction module 851, syntax modules 852A, 852B, and
852C, event identifier modules 853A, 853B, and 853C, event
parameter modules 854A, 854B, and 854C, event scoring module 856,
event detection module 857, and device 813. Clip extraction module
851, syntax modules 852A, 852B, and 852C, event identifier modules
853A, 853B, and 853C, event parameter modules 854A, 854B, and 854C,
event scoring module 856, event detection module 857, and device
813 can be included in, integrated with, and/or interoperate with
signal ingestion modules 101 (e.g., stream content ingestion
modules 176 and stream processing modules 183) and/or event
detection infrastructure 103.
[0172] Clip extraction module 851 can receive streams 802A, 802B,
and 802C from channels 801A, 801B, and 801C respectively. Streams
802A, 802B, and 802C can be included in streaming signals 173. Clip
extraction module 851 can extract clips 804A, 804B, and 804C from
streams 802A, 802B, and 802C respectively. Syntax modules 852A,
852B, and 852C identify sub-clips 806A, 806B, and 806C from clips
804A, 804B, and 804C respectively.
[0173] Sub-clips 806A, 806B, and 806C are locations in clips 804A,
804B, and 804C respectively that are more likely to include event
identifiers. Syntax modules 852A, 852B, and 852C can identify
sub-clips 806A, 806B, and 806C based on syntax (e.g., 601, 602,
etc.) of the geographic location, jurisdiction, corporation, etc.
associated with each of channels 801A, 801B, and 801C respectively.
Event identifier modules 853A, 853B, and 853C attempt to identify
event identifiers in sub-clips 804A, 804B, and 804C respectively.
As depicted, event identifier module 853A identifies event
identifier 807A and event identifier module 853B identifies event
identifier 807B,
[0174] Event identifier module 853C does not identify an event
identifier in sub-clip 806. As such, it is unlikely that clip 804C
contains event information. Accordingly, no further computing
resources are allocated to process clip 804C.
[0175] Further resources can be allocated to event parameter
modules 854A and 854B. Event parameter module 854A can extract
event parameters 803A from clip 804A using the allocated resources.
Similarly, parameter module 854B can extract event parameters 803B
from clip 804B using the allocated resources. Event scoring module
856 can compute event score 809A from event parameters 803A.
Similarly, event scoring module 856 can compute event score 809B
from event parameters 803B.
[0176] Event detection module 857 can determine that event score
809B is to low and does not indicate an event (e.g., event score
809B is below an event score threshold). On the other hand, event
detection module 857 can detect event 811 based on event score 809A
(e.g., event score 809A can exceed the event score threshold).
Event detection module 857 can notify 812 device 813 of the
occurrence of event 811. Other action can also be taken in response
to detecting event 811.
[0177] Event Score Generation Additional Example
[0178] FIG. 9 illustrates an example computer architecture 900 that
facilitates detecting an event from scores generated from ingested
signals. As depicted, computer architecture 900 includes portion
selector 901, score calculator 902, parameter extractor 904, event
score calculator 906, and resource allocator 907. Portion selector
901, score calculator 902, parameter extractor 904, event score
calculator 906, and resource allocator 907 can be included in,
integrated with, and/or interoperate with signal ingestion modules
101 and/or event detection infrastructure 103.
[0179] In general, portion selector 901 is configured select a
portion of a signal from within the signal. The signal can be of
virtually any signal type, including signal types ingestible by
signal ingestion modules 101 or otherwise described. Portion score
calculator 902 is configured to calculate a portion score for a
selected portion using any of the described mechanisms.
[0180] Parameter extractor 904 is configured to extract parameters
from another signal using any of the described mechanisms. The
other signal can be of virtually any signal type, including signal
types ingestible by signal ingestion modules 101 or otherwise
described. Event score calculator 906 is configured to calculate an
event score from extracted parameters using any of the described
mechanisms.
[0181] The signal and the other signal can be associated with,
relevant to, or even related to one another by one or more of: a
Time dimension, a Location dimension, or a Context dimension.
[0182] Resource allocator 907 is configured to determine when
additional (e.g., processor, memory, storage, etc.) resources are
to be allocated to process the other signal. When additional
resources are to be allocated, resource allocator allocates the
additional resources to parameter extractor 904 and event score
calculator 906. In one aspect, resource allocator 907 determines
when additional resources are to be allocated based on a portion
score. When a portion score exceeds a threshold portion score,
additional resources can be allocated. When a portion score does
not exceed a threshold portion score, additional resources are not
allocated. As such, additional resources can be allocated when
there is increased likelihood of a signal including event
information. Otherwise, resources are conserved to allocate for
other functions.
[0183] FIG. 10 illustrates a flow chart of an example method 1000
for detecting an event from scores generated from ingested signals.
Method 1000 will be described with respect to the components and
data depicted in computer architecture 900.
[0184] Method 1000 includes ingesting a signal (1001). For example,
portion selector 901 can ingest communication signal 921 (e.g., a
streaming signal, a non-streaming signal, a Web signal, a social
signal, a database signal, etc.) from raw signals 173. Method 1000
includes selecting a portion of the signal from within the signal
(1002). For example, portion selector 901 can select signal portion
922 from within signal 921.
[0185] Method 10000 includes computing a first score from the
selected portion (or characteristics thereof) and indicating a
likelihood of the signal including information related to an event
type (1003). For example, score calculator 902 can compute portion
926 from characteristics of signal portion 922. Portion score 922
indicates a likelihood of signal 921 including event information.
In one aspect, portion selector 901 identifies a portion of signal
921 with the highest probability of having an event identifier.
Score calculator 902 analyzes portion for an event identifier and
computes portion score 926 based on the event identifier.
[0186] Method 1000 includes determining further processing of
another signal is warranted based on the indicated likelihood
(1004). For example, resource allocator 907 can determine that
processing of signal 931 is warranted based on a likelihood
indicated by portion score 926. Method 1000 includes allocating
computing resources to further process the communication clip
(1005). For example, resource allocator 907 can allocate computing
resources 927 (e.g., memory resources, processing resources, etc.)
to parameter extractor 904 and event score calculator 906.
[0187] Method 1000 includes ingesting the other signal (1006). For
example, parameter extractor 904 can ingest signal 931 (e.g., a
streaming signal, a non-streaming signal, a Web signal, a social
signal, a database signal, etc.) from raw signals 173. Method 1000
includes accessing parameters associated with the other signal
(1007). For example, parameter extractor 904 can access parameters
932 associated with (and possibly from) signal 931 (or
characteristics thereof). Parameter extractor 901 can access
parameters 932 utilizing allocated computing resources 927.
[0188] Method 1000 includes computing a second score from the
parameters utilizing the allocated computing resources (1008). For
example, event score calculator 906 can calculate event score 934
from parameters 932 (utilizing computing resources 427). Event
score calculator 906 can send event score calculator 906 to other
modules of event detection infrastructure 103.
[0189] Method 1000 includes detecting a previously unidentified
event of the event type utilizing the allocated resources and based
on the second score (1009). For example, event detection
infrastructure 103 can detect an event based on event score 934
(and utilizing computing resources 927). Entities can be notified
of the detected event. The detected event can also provide a basis
for rerouting delivery vehicles or performing other actions.
[0190] In one aspect, an ingested signal and/or detected event
relates to availability of beds in public and/or private
care/treatment facilities. For example, a database signal can be
ingested and used in detecting an available bed at a care/treatment
facility. A database signal (or portion thereof or associated
parameters) can be used in computation of a first score or a second
score.
[0191] As such, a unified interface can handle incoming signals and
content of any kind. The interface can handle live extraction of
signals across dimensions of time, location, and context. In some
aspects, heuristic processes are used to determine one or more
dimensions. Acquired signals can include text and images as well as
live-feed binaries, including live media in audio, speech, fast
still frames, video streams, etc.
[0192] Signal normalization enables the world's live signals to be
collected at scale and analyzed for detection and validation of
live events happening globally. A data ingestion and event
detection pipeline aggregates signals and combines detections of
various strengths into truthful events. Thus, normalization
increases event detection efficiency facilitating event detection
closer to "live time" or at "moment zero".
[0193] Communication signals, including streaming audio signals,
can be processed in a resource conscious manner to attempt to
detect events. When detecting an event from a communication signal
is more likely based on partial analysis, additional resources can
be allocated for further processing. On the other hand, when
detecting an event from a communication signal is less likely based
on partial analysis, additional resources are not allocated.
[0194] Embodiments of the invention can include any combination and
permutation of the various system components and the various method
processes, wherein one or more instances of the method and/or
processes described herein can be performed asynchronously (e.g.,
sequentially), concurrently (e.g., in parallel), or in any other
suitable order by and/or using one or more instances of the
systems, elements, and/or entities described herein.
[0195] The present described aspects may be implemented in other
specific forms without departing from its spirit or essential
characteristics. The described aspects are to be considered in all
respects only as illustrative and not restrictive. The scope is,
therefore, indicated by the appended claims rather than by the
foregoing description. All changes which come within the meaning
and range of equivalency of the claims are to be embraced within
their scope.
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